Within the Swiss Personalized Health Network ([SPHN](https://sphn.ch)) and related national initiatives researchers use patient data (i.e., confidential human data) in their research projects. Dealing with confidential human data requires awareness of data privacy, respective laws and information security. This course explains what should be done in practice to protect the patients’ privacy when performing biomedical research on human data.
Sequence-structure-function relationships of proteins are central to a comprehensive understanding of cellular biology. However, many proteins lack direct and detailed information regarding structure, function, and complex formation. This knowledge gap can be overcome through clearly defined relationships between proteins and the integration of existing data.
Participants will be introduced to primarily web-based tools, designed for life scientists without substantial computational training, for integration and inference of protein structure and function data. The lectures and workshops will be led by the SIB developers of these methods, with specific emphasis on translating this training to the participants' own research questions.
The aim of this course is to familiarise the participants with long read (also called “third generation”) sequencing technologies, their applications and the bioinformatics tools used to assemble this kind of data. Multiple sequencing platforms, including Pacific Biosciences and Oxford Nanopore MinION, are now available to generate reads that are several kilobases-long. It is also possible to assemble Illumina reads to generate in-silico long reads. These improvements have greatly facilitated the assembly of genomes but some other applications are emerging, for example, for haplotype phasing, or for the study of alternative splicing using RNA-seq.
This course will be composed of an introduction to the techniques and data analysis methods, a minisymposium and a hands-on session. The minisymposium will consist of short presentations by SIB researchers on the applications of these technologies. It will be followed by a panel discussion between speakers and the audience, letting the opportunity to debate on the advantages and pitfalls of these technologies for research projects. The hands-on session will consist of computer exercises that will enable the participants to familiarize with real datasets from different technologies and the bioinformatics tools to assemble genomes.
R is a complete and flexible system for statistical analysis which has become a tool of choice for biologists and biomedical scientists, who need to analyze and visualize large amounts of data. One reason for this success is the availability of many contributed packages, which are available freely and can be installed and run directly from R. In bioinformatics, in particular, most published papers include a link to an R package implementing the methods described in the article. This "First Steps with R" course is addressed to beginners wanting to become familiar with the R environment and master the most common commands to be able to start exploring their own datasets.
The course will introduce the basic concepts of biological network analysis and provide practical instruction on commonly used bioinformatics tools to analyse and visualize biological networks. The course will start with a short symposium on biological network analysis covering network analysis and theory, visualization of complex networks and application to real biological problems. The symposium will be followed by theoretical and practical sessions where course participants will gain practical knowledge on how to perform different biological network analyses and how to visualize and interpret the results of such analyses.
The course will focus on learning and internalizing the practices of unit testing, refactoring, and version control through hands-on experience. The first morning will start with an introduction into these concepts and tools used to support them. In the afternoon, we will transition to a code clinic and work together in small groups applying these practices to make improvements to code brought by participants. The second day will continue with the code clinic.